Machine learning approaches offer some of the most cost-effective approaches to building predictive models (e.g., classifiers) in a broad range of applications in computational bio...
Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome fo...
Anelia Angelova, Yaser S. Abu-Mostafa, Pietro Pero...
For complex tasks such as parse selection, the creation of labelled training sets can be extremely costly. Resource-efficient schemes for creating informative labelled material mu...
We investigate the use of self-predicting neural networks for autonomous robot learning within noisy or partially predictable environments. A benchmark experiment is performed in ...
James B. Marshall, Neil K. Makhija, Zachary D. Rot...
We investigate incremental word learning in a Hidden Markov Model (HMM) framework suitable for human-robot interaction. In interactive learning, the tutoring time is a crucial fac...